1. Field of the Invention
The present invention relates generally to the field of electronic medical records, and more particularly to an information system for the storage, handling, processing, versioning, auditing and security of electronic medical records.
2. Description of the Related Art
An electronic medical record (EMR) is a term used to describe patient health information contained in a record for use by a physician at a doctor's office. Correspondingly, an electronic patient record (EPR) describes patient health information intended for use by the patient; and an electronic health record (EHR) describes patient health information used in a hospital setting. Each of these terms is used interchangeably around the world, e.g. an EMR in Canada corresponds to an EPR in England. The present invention is designed to work with any of these record types.
The International Statistical Classification of Diseases and Related Health Problems, or ICD, attempts to classify diseases and has created codes for a wide variety of signs, symptoms, abnormal findings, complaints, social circumstances, and external causes of injury or disease. Every health condition can be assigned a unique category and given a code, up to six characters long. Categories include sets of similar diseases.
For example, using ICD-9 coding, a diabetic is coded as 250. What this means is that when care providers communicate, “250” is understood as “diabetes”. Finer granularity is achieved by adding an extra digit, e.g. “2501” is understood as ‘Diabetes with coma’. ICD is an example of a “two-attribute granule”. That is, the code “250” and the description “Diabetes”.
Unfortunately, this is inadequate for EMR information systems for a number of reasons. First, one can find the code to describe a disease only about 50% of the time because either the correct code cannot be found easily or because a code does not exist for that condition. Second, there are no additional attributes to describe the condition further. In the diabetic example, other information such as the date of diagnosis, severity, or whether there is any family history etc. cannot be described within the above schema.
Systematized Nomenclature of Medicine, or SNOMED, is a systematically organized collection of medical terminology covering most areas of clinical information such as diseases, findings, procedures, micro-organisms, pharmaceuticals etc. It allows a standardized way of indexing, storing, retrieving, and aggregating clinical data so they can be accessed and understood by disparate medical information systems. It also helps to organize the content of medical records, and to reduce the variability in the way data is captured, encoded and used for clinical care of patients and research.
While SNOMED does a good job of defining concepts, their relationships, and primitive qualifiers in addition to providing a larger number of medical terms than ICD (370,000 versus 10,000), it falls short in clinical encoding as the number of attributes used to describe the encoded concept limits it. This limitation requires SNOMED to create a new concept and code for every exception that does not have an adequate qualifier to describe it.
Conventional EMR information systems comprise a relational database that includes a complicated assortment of interrelated tables, e.g. such systems can have between 500 to 4000 tables. It is well known that in order to add a column of information to the database it will cost upwards of $1 million, due to the complexity of the interrelations between the tables.
Over the years a mountain of medical information has been collected in EMR information systems. There is an advantage in data mining this information in order to aid diagnosis and treatment of a patient's condition, and for statistical analysis on the medical health of the population as a whole.
Complex and time consuming search queries are required in conventional EMR information systems since this mountain of data is distributed over 500 to 4000 relational database tables. This has inhibited the discovery of medical correlations due to the sheer complexity in locating pertinent information, and has lead to the limited access to this information by health professionals and researchers due to the time consuming nature in performing queries on such complicated data storage systems.
Conventional EMR information systems are static in nature with regard to the information they store. This limitation is related to the fixed nature of the relational database structure used. For example, patient demographic information has conventionally included the name of the patient, and their home address. As can be imagined, this information quite often changes throughout the life of the patient. The previous name and address information is quite often discarded when the new name and address are included in the EMR of the patient, and at the very least the previous historical information becomes inaccessible through the EMR information system.
This is problematic since quite often historical medical information may be associated with the name of the patient, and removing the history of the patient's name may result in losing part of the medical history of the patient. There is a great disadvantage in conventional EMR information systems in not keeping a complete history of a patient's EMR, and in not being able to view the state of a patient's EMR at a particular point in time.
There is a need for an improved EMR information system that eliminates the complexity of conventional EMR information system relational database structures. Such an improved EMR information system would facilitate data mining activities in a simple and timely manner, as well as provide a mechanism to record and playback the complete history of a patient's EMR. In addition, such a new improved EMR information system would include a universal encoding methodology for the recordation of medical observations.